Spatial And Temporal Dynamics Of Glossina Pallidipes In Nguruman National Park (kenya).

Abstract: Background: Glossina pallidipes is a significant vector of trypanosomiasis for humans and livestock across Sub-Saharan Africa. Nguruman national park in Kenya is regarded as a potential location for targeted vector control activity. We screened flies from three locations in Nguruman in 2003, 2009, and 2015 using 13 microsatellite loci to assess population structures of G. pallidipes. Methods: We acquired Tsetse samples from three sites across Nguruman in Kenya at three time points. We then performed genetic analyses on 267 flies using 13 microsatellite loci and evaluated genetic structure of Tsetses across a geographic distance of about 22 km and changes associated with sampling at 3 different time points separated by a maximum of 12 years. Results: We found that samples collected in different locations separated by up to 22 km within the same year were not significantly differentiated from one another. However, samples separated temporally were significantly differentiated. Conclusion: Previous studies on the population structure of G.pallidipes in East-Africa have found population structure at variable geographic scales, with population structure across just 5 km in some cases. Our study shows that in Nguruman, the genetic structure of G.pallidipes is dominated by a temporal signal in samples collected across 12 years, but do not display population structure at the small spatial scale investigated

Algorithm-assisted discovery of an intrinsic order among mathematical constants

In recent decades, a growing number of discoveries in fields of mathematics have been assisted by computer algorithms, primarily for exploring large parameter spaces that humans would take too long to investigate. As computers and algorithms become more powerful, an intriguing possibility arises - the interplay between human intuition and computer algorithms can lead to discoveries of novel mathematical concepts that would otherwise remain elusive. To realize this perspective, we have developed a massively parallel computer algorithm that discovers an unprecedented number of continued fraction formulas for fundamental mathematical constants. The sheer number of formulas discovered by the algorithm unveils a novel mathematical structure that we call the conservative matrix field. Such matrix fields (1) unify thousands of existing formulas, (2) generate infinitely many new formulas, and most importantly, (3) lead to unexpected relations between different mathematical constants, including multiple integer values of the Riemann zeta function. Conservative matrix fields also enable new mathematical proofs of irrationality. In particular, we can use them to generalize the celebrated proof by Ap\'ery for the irrationality of $\zeta(3)$. Utilizing thousands of personal computers worldwide, our computer-supported research strategy demonstrates the power of experimental mathematics, highlighting the prospects of large-scale computational approaches to tackle longstanding open problems and discover unexpected connections across diverse fields of science.Comment: 21 pages, 6 figures, and 1 table; with 9 appendix sections totaling 12 pages, 1 figure, and 4 table

Increasing JAK/STAT Signaling Function of Infant CD4+ T Cells during the First Year of Life

Most infant deaths occur in the first year of life. Yet, our knowledge of immune development during this period is scarce and derived from cord blood (CB) only. To more effectively combat pediatric diseases, a deeper understanding of the kinetics and the factors that regulate the maturation of immune functions in early life is needed. Increased disease susceptibility of infants is generally attributed to T helper 2-biased immune responses. The differentiation of CD4+ T cells along a specific T helper cell lineage is dependent on the pathogen type, and on costimulatory and cytokine signals provided by antigen-presenting cells. Cytokines also regulate many other aspects of the host immune response. Therefore, toward the goal of increasing our knowledge of early immune development, we defined the temporal development of the Janus kinase (JAK)/signal transducers and activators of transcription (STAT) signaling function of CD4+ T cells using cross-sectional blood samples from healthy infants ages 0 (birth) to 14 months. We specifically focused on cytokines important in T cell differentiation (IFN-γ, IL-12, and IL-4) or in T cell survival and expansion (IL-2 and IL-7) in infant CD4+ T cells. Independent of the cytokine tested, JAK/STAT signaling in infant compared to adult CD4+ T cells was impaired at birth, but increased during the first year, with the most pronounced changes occurring in the first 6 months. The relative change in JAK/STAT signaling of infant CD4+ T cells with age was distinct for each cytokine tested. Thus, while about 60% of CB CD4+ T cells could efficiently activate STAT6 in response to IL-4, less than 5% of CB CD4+ T cells were able to activate the JAK/STAT pathway in response to IFN-γ, IL-12 or IL-2. By 4–6 months of age, the activation of the cytokine-specific STAT molecules was comparable to adults in response to IL-4 and IFN-γ, while IL-2- and IL-12-induced STAT activation remained below adult levels even at 1 year. These results suggest that common developmental and cytokine-specific factors regulate the maturation of the JAK/STAT signaling function in CD4+ T cells during the first year of life

Identification of Common Differentially Expressed Genes in Urinary Bladder Cancer

BACKGROUND: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. PURPOSE: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. METHODOLOGY/PRINCIPAL FINDINGS: BC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways. CONCLUSIONS/SIGNIFICANCE: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers

How genomics can help biodiversity conservation

The availability of public genomic resources can greatly assist biodiversity assessment, conservation, and restoration efforts by providing evidence for scientifically informed management decisions. Here we survey the main approaches and applications in biodiversity and conservation genomics, considering practical factors, such as cost, time, prerequisite skills, and current shortcomings of applications. Most approaches perform best in combination with reference genomes from the target species or closely related species. We review case studies to illustrate how reference genomes can facilitate biodiversity research and conservation across the tree of life. We conclude that the time is ripe to view reference genomes as fundamental resources and to integrate their use as a best practice in conservation genomics.info:eu-repo/semantics/publishedVersio

The era of reference genomes in conservation genomics

Progress in genome sequencing now enables the large-scale generation of reference genomes. Various international initiatives aim to generate reference genomes representing global biodiversity. These genomes provide unique insights into genomic diversity and architecture, thereby enabling comprehensive analyses of population and functional genomics, and are expected to revolutionize conservation genomics

The era of reference genomes in conservation genomics

Progress in genome sequencing now enables the large-scale generation of reference genomes. Various international initiatives aim to generate reference genomes representing global biodiversity. These genomes provide unique insights into genomic diversity and architecture, thereby enabling comprehensive analyses of population and functional genomics, and are expected to revolutionize conservation genomics

The era of reference genomes in conservation genomics

info:eu-repo/semantics/publishedVersio

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification